In [1]:
import numpy as np
import pandas as pd
from pandas import DataFrame
from datetime import datetime
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
import seaborn as sns
%matplotlib inline
In [ ]:
!ls
In [2]:
#Available data up to 2/26/2020
confirmed = pd.read_csv('time_series_19-covid-Confirmed.csv').fillna(0)
death = pd.read_csv('time_series_19-covid-Deaths.csv').fillna(0)
recovered = pd.read_csv('time_series_19-covid-Recovered.csv').fillna(0)
In [3]:
#convert column name to datetime format
confirmedtimeframe = confirmed.iloc[:,5:len(confirmed.columns)]
confirmedtimeframe.columns = pd.to_datetime(confirmedtimeframe.columns).date
deathtimeframe = death.iloc[:,5:len(death.columns)]
deathtimeframe.columns = pd.to_datetime(deathtimeframe.columns).date
recoveredtimeframe = recovered.iloc[:,5:len(recovered.columns)]
recoveredtimeframe.columns = pd.to_datetime(recoveredtimeframe.columns).date
In [4]:
confirmed = pd.concat([confirmed.iloc[:,0:5],confirmedtimeframe],axis = 1,sort=True)
death = pd.concat([death.iloc[:,0:5], deathtimeframe],axis = 1,sort=True)
recovered = pd.concat([recovered.iloc[:,0:5], recoveredtimeframe],axis = 1,sort=True)
In [5]:
#Melt indivdual timeframe data to values under 'Date'
confirmed = confirmed.melt(id_vars=["Province/State", "Country/Region",'Lat','Long','Code'], var_name="Date", value_name="Confirmed")
death = death.melt(id_vars=["Province/State", "Country/Region",'Lat','Long','Code'], var_name="Date", value_name="Death")
recovered = recovered.melt(id_vars=["Province/State", "Country/Region",'Lat','Long','Code'], var_name="Date", value_name="Recovered")
In [6]:
#Consolidate dataframe that's ready to be used
data = pd.concat([confirmed, death.iloc[:,6],recovered.iloc[:,6]], axis =1, sort = True)
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#separate China region from and other regions worldwide
chinaregion = data.loc[(data['Country/Region'] == 'Mainland China')|(data['Country/Region'] == 'Taiwan')|
(data['Country/Region'] =='Hong Kong')|(data['Country/Region'] =='Macau'),:]
ind = data.loc[(data['Country/Region'] == 'Mainland China')|(data['Country/Region'] == 'Taiwan')|
(data['Country/Region'] =='Hong Kong')|(data['Country/Region'] =='Macau'),:].index
nonchinaregion = data.drop(data.index[ind],inplace = False).reset_index(drop = True)
In [8]:
chinaregionpivot = pd.pivot_table(chinaregion,index=["Country/Region","Province/State",'Date'],
values = ['Confirmed','Recovered','Death'],
aggfunc = np.mean)
In [10]:
nonchinaregionpivot = pd.pivot_table(nonchinaregion,index=["Country/Region","Province/State",'Date'],
values = ['Confirmed','Recovered','Death'],
aggfunc = np.mean)
In [11]:
#Function to extract timeframe table per location
#region: China: chinaregionpivot/nonchinaregionpivot
#country: Country/Region
#state: Province/State
def getlocationdata(region, country, state):
location = region.loc[(country, state,), :]
return location
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#Insert region, country, state values for the location in question and rename the timeframe table
Hubei = getlocationdata(chinaregionpivot, 'Mainland China', 'Hubei')
In [13]:
plt.figure(figsize=(16,8))
Hubei.loc[:,'Confirmed'].sort_index().plot.line(color='b',marker='o',linestyle ='-.')
plt.ylabel('Number of Patient')
plt.title('Confirmed in Hubei')
plt.legend()
Out[13]:
In [14]:
plt.figure(figsize=(16,8))
Hubei.loc[:,'Death'].sort_index().plot.line(color='r',marker='x',linestyle ='--')
Hubei.loc[:,'Recovered'].sort_index().plot.line(color='g',marker='o',linestyle ='--')
plt.ylabel('Number of Patient')
plt.title('Death and Recovery in Hubei')
plt.legend()
Out[14]:
In [16]:
chinaregion['Date'] = pd.to_datetime(chinaregion['Date'])
chinaregion['Date'] = chinaregion['Date'].dt.strftime('%b.%d')
nonchinaregion['Date'] = pd.to_datetime(nonchinaregion['Date'])
nonchinaregion['Date'] = nonchinaregion['Date'].dt.strftime('%b.%d')
In [46]:
#Get the latest data
chinalatest = pd.DataFrame(chinaregion.groupby(['Province/State','Country/Region'])['Lat','Long','Code','Confirmed','Recovered','Death'].agg('max')).sort_values(by=['Confirmed'], ascending = False).reset_index()
In [18]:
fig = px.bar(chinaregion, x='Province/State', y='Confirmed',color = 'Province/State',color_discrete_sequence= px.colors.qualitative.Set3,
animation_frame="Date",animation_group="Province/State",hover_data=['Confirmed'], title = 'Confirmed in China Region')
fig.show()
In [19]:
#fig = px.bar(chinalatest.loc[chinalatest['Province/State'] != 'Hubei',:], x='Province/State', y='Confirmed',color = 'Province/State',
# color_discrete_sequence= px.colors.qualitative.Set3,
# hover_data=['Confirmed'], title = 'Confirmed in China Region Excluding Hubei')
fig = px.bar(chinaregion.loc[chinaregion['Province/State'] != 'Hubei',:], x='Province/State', y='Confirmed',color = 'Province/State',
color_discrete_sequence= px.colors.qualitative.Set3, animation_frame="Date",animation_group="Province/State",
hover_data=['Confirmed'], title = 'Confirmed in China Region Excluding Hubei')
fig.show()
In [41]:
nonchinalatest = pd.DataFrame(nonchinaregion.groupby(['Country/Region'])['Lat','Long','Code','Confirmed','Recovered','Death'].agg('max')).sort_values(by = 'Confirmed', ascending = False).reset_index()
In [21]:
#Others: Diamond Princess Cruise Ship
#fig = px.bar(nonchinalatest.loc[nonchinalatest['Country/Region'] != 'Others',:], x='Country/Region', y='Confirmed',color = 'Country/Region',
# color_discrete_sequence= px.colors.qualitative.Set3,
# hover_data=['Confirmed'], title = 'Confirmed outside China')
fig = px.bar(nonchinaregion, x='Country/Region', y='Confirmed',color = 'Country/Region',
color_discrete_sequence= px.colors.qualitative.Set3,animation_frame="Date",animation_group="Country/Region",
hover_data=['Confirmed'], title = 'Confirmed outside China')
fig.show()
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chinalatest['Death Rate'] = chinalatest['Death']/chinalatest['Confirmed']
chinalatest['Recover Rate'] = chinalatest['Recovered']/chinalatest['Confirmed']
nonchinalatest['Death Rate'] = nonchinalatest['Death']/nonchinalatest['Confirmed']
nonchinalatest['Recover Rate'] = nonchinalatest['Recovered']/nonchinalatest['Confirmed']
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#Death v.s. Confirmed in other part of China
fig = px.scatter(chinalatest.loc[chinalatest['Province/State'] != 'Hubei',:], x="Confirmed", y="Death", size = 'Recover Rate',
color="Province/State",color_discrete_sequence= px.colors.qualitative.Plotly,size_max=60,
title = 'Deaths in Confirmed in Other Part of China')
fig.show()
In [24]:
#Death v.s. Confirmed outside China
fig = px.scatter(nonchinalatest, x="Confirmed", y="Death", size = 'Recover Rate',
color="Country/Region",color_discrete_sequence= px.colors.qualitative.Plotly,size_max=60,
title = 'Deaths in Confirmed outside China')
fig.show()
In [25]:
#region: China: chinaregionpivot/nonchinaregionpivot
#country: Country/Region
#state: Province/State (enter 0 if shows '0')
def getincremental(region, country, state):
incc = []
incd = []
incr = []
for i in range(len(getlocationdata(region, country, state))-1):
c = getlocationdata(region, country, state)['Confirmed'][i+1]-getlocationdata(region, country, state)['Confirmed'][i]
d = getlocationdata(region, country, state)['Death'][i+1]-getlocationdata(region, country, state)['Death'][i]
r = getlocationdata(region, country, state)['Recovered'][i+1]-getlocationdata(region, country, state)['Recovered'][i]
incc.append(c)
incd.append(d)
incr.append(r)
dic = {'Incremental Confirmed': incc, 'Incremental Death': incd, 'Incremental Recovered': incr}
incremental = pd.DataFrame(data = dic)
incremental['Day'] = np.arange(1,len(incremental)+1,1)
return incremental
In [26]:
#Specify the region you want to explore
region = chinaregionpivot
country = 'Mainland China'
state = 'Hubei'
incremental = getincremental(region, country, state)
incremental
Out[26]:
In [27]:
#Plot trend of incremental confirms
fig = go.Figure()
fig.add_trace(go.Scatter(x = incremental['Day'], y=incremental['Incremental Confirmed'], name='Confirmed',
mode='lines+markers',line=dict(color='firebrick', width=2, dash='dash')))
if state != 0:
fig.update_layout(title='Incremental Confirms in ' + state,
xaxis_title='Days',
yaxis_title='Confirms')
else:
fig.update_layout(title='Incremental Confirms in ' + country,
xaxis_title='Days',
yaxis_title='Confirms')
fig.show()
In [28]:
fig = go.Figure()
fig.add_trace(go.Scatter(x = incremental['Day'], y=incremental['Incremental Death'],
name='Death', mode='lines+markers',line=dict(color='firebrick', width=2, dash='dot')))
fig.add_trace(go.Scatter(x = incremental['Day'], y=incremental['Incremental Recovered'],
name='Recovered', mode='lines+markers',line=dict(color='royalblue', width=2,dash='dot')))
if state != 0:
fig.update_layout(title='Incremental Death and Recovery in ' + state,
xaxis_title='Days',
yaxis_title='Number of Patients')
else:
fig.update_layout(title='Incremental Death and Recovery in ' + country,
xaxis_title='Days',
yaxis_title='Number of Patients')
fig.show()
In [104]:
#Spreading in all China regions
fig = px.scatter_geo(chinaregion, lat ="Lat", lon = 'Long',
color="Confirmed",size = 'Confirmed', animation_frame="Date",
hover_name="Province/State", size_max=20,
color_continuous_scale=[[0, 'rgb(255,160,122)'],
[0.01,"rgb(255,99,71)"],
[0.02,"rgb(220,20,60)"],
[0.2,"rgb(178,34,34)"],
[0.6,"rgb(165,42,42)"],
[0.8,"rgb(139,0,0)"],
[1.0,"rgb(128,0,0)"]])
fig.update_geos(
showcoastlines=True, coastlinecolor="Black",
showland=True, landcolor="LightBlue",
showocean=True, oceancolor="CornflowerBlue",
)
fig.update_layout(title='Confirmed in China thru Timeline')
fig.show()
In [103]:
#Spreading in other China regions (Excluding Hubei)
fig = px.scatter_geo(chinaregion.loc[chinaregion['Province/State'] != 'Hubei',:], lat ="Lat", lon = 'Long',
color="Confirmed",size = 'Confirmed', animation_frame="Date",
hover_name="Province/State", size_max=10,
color_continuous_scale=[[0, 'rgb(255,160,122)'],
[0.1,"rgb(255,99,71)"],
[0.2,"rgb(220,20,60)"],
[0.4,"rgb(178,34,34)"],
[0.6,"rgb(165,42,42)"],
[0.8,"rgb(139,0,0)"],
[1.0,"rgb(128,0,0)"]],)
fig.update_geos(
showcoastlines=True, coastlinecolor="Black",
showland=True, landcolor="LightBlue",
showocean=True, oceancolor="CornflowerBlue",
)
fig.update_layout(title='Confirmed in China (excluding Hubei) thru Timeline')
fig.show()
In [101]:
#Spreading in all regions outside China
fig = px.scatter_geo(nonchinaregion, lat ="Lat", lon = 'Long',
color="Confirmed",size = 'Confirmed',animation_frame="Date",
hover_name="Country/Region", size_max=20,
color_continuous_scale=[[0, 'rgb(255,160,122)'],
[0.01,"rgb(255,99,71)"],
[0.02,"rgb(220,20,60)"],
[0.2,"rgb(178,34,34)"],
[0.6,"rgb(165,42,42)"],
[0.8,"rgb(139,0,0)"],
[1.0,"rgb(128,0,0)"]])
fig.update_geos(
showcoastlines=True, coastlinecolor="Black",
showland=True, landcolor="LightBlue",
showocean=True, oceancolor="CornflowerBlue",
)
fig.update_layout(title='Confirmed outside China thru Timeline')
fig.show()
In [100]:
#Spreading in all named regions outside China
fig = px.scatter_geo(nonchinaregion.loc[(nonchinaregion['Country/Region'] != 'Others')|(nonchinaregion['Country/Region'] != 0),:],
lat ="Lat", lon = 'Long',
color="Confirmed",size = 'Confirmed', animation_frame="Date",
hover_name="Province/State", size_max=30,
color_continuous_scale=[[0, 'rgb(255,160,122)'],
[0.01,"rgb(255,99,71)"],
[0.02,"rgb(220,20,60)"],
[0.2,"rgb(178,34,34)"],
[0.6,"rgb(165,42,42)"],
[0.8,"rgb(139,0,0)"],
[1.0,"rgb(128,0,0)"]],)
fig.update_geos(
showcoastlines=True, coastlinecolor="Black",
showland=True, landcolor="LightBlue",
showocean=True, oceancolor="CornflowerBlue",
)
fig.update_layout(title='Confirmed Outside China thru Timeline (only includes named regions)')
fig.show()
In [47]:
latestdata = pd.concat([chinalatest,nonchinalatest],axis = 0,sort=False)
latestbycode = pd.DataFrame(latestdata.groupby(['Code','Country/Region'])['Country/Region','Code','Confirmed','Recovered','Death'].agg('sum')).sort_values(by=['Confirmed'], ascending = False).reset_index()
In [98]:
fig = go.Figure(data=go.Choropleth(
locations = latestbycode['Code'],
z = latestbycode['Confirmed'],
text = latestbycode['Country/Region'],
colorscale = [[0, 'rgb(255,222,173)'],
[0.001,"rgb(255,99,71)"],
[0.005,"rgb(165,42,42)"],
[0.2,"rgb(178,34,34)"],
[1.0,"rgb(128,0,0)"]],
autocolorscale=False,
reversescale=False,
marker_line_color='darkgray',
marker_line_width=0.5,
colorbar_title = 'Confirmed',
))
fig.update_geos(
showcoastlines=True, coastlinecolor="Black",
showland=True, landcolor="LightBlue",
showocean=True, oceancolor="CornflowerBlue",
)
fig.update_layout(title='Latest Worldwide Confirmed')
fig.show()
In [99]:
fig = go.Figure(data=go.Choropleth(
locations = latestbycode.loc[latestbycode['Code'] != 'CHN',:]['Code'],
z = latestbycode.loc[latestbycode['Code'] != 'CHN',:]['Confirmed'],
text = latestbycode.loc[latestbycode['Code'] != 'CHN',:]['Country/Region'],
colorscale = [[0, 'rgb(255,160,122)'],
[0.01,"rgb(255,99,71)"],
[0.02,"rgb(220,20,60)"],
[0.2,"rgb(178,34,34)"],
[0.6,"rgb(165,42,42)"],
[0.8,"rgb(139,0,0)"],
[1.0,"rgb(128,0,0)"]],
autocolorscale=False,
reversescale=False,
marker_line_color='darkgray',
marker_line_width=0.5,
colorbar_title = 'Confirmed',
))
fig.update_geos(
showcoastlines=True, coastlinecolor="Black",
showland=True, landcolor="LightBlue",
showocean=True, oceancolor="CornflowerBlue",
)
fig.update_layout(title='Latest Confirmed Outside China')
fig.show()